Learning device, inference device, and learned model

ABSTRACT

With respect to an inference method performed by at least one processor, the method includes inputting, by the at least one processor, into a learned model, non-processed object image data of a second object and data related to a second process for the second object, and inferring, by the at least one processor using the learned model, processed object image data of the second object on which the second process has been performed. The learned model has been trained so that an output obtained in response to non-processed object image data of a first object and data related to a first process for the first object being input approaches processed object image data of the first object on which the first process has been performed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of InternationalApplication No. PCT/JP2019/033168 filed on Aug. 23, 2019, anddesignating the U.S., which is based upon and claims priority toJapanese Patent Application No. 2018-164931, filed on Sep. 3, 2018, theentire contents of which are incorporated herein by reference.

BACKGROUND 1. Technical Field

The disclosure herein relates to a learning device, an inference device,and a learned method.

2. Description of the Related Art

Semiconductor manufacturers generate physical models of respectivemanufacturing processes (e.g., dry etching and deposition) and performsimulations to search for optimal recipes and to adjust processparameters.

With respect to the above, because the behaviors of semiconductormanufacturing processes are complicated, there are events that cannot berepresented by physical models, thereby limiting the accuracy of thesimulations. Thus, in recent years, the application of models learned byusing machine learning is studied as an alternative to simulators basedon physical models.

Here, learned models have an advantage that it is not necessary todefine each event in the semiconductor manufacturing process by using aphysical equation or the like, as in physical models, and it is expectedthat simulation accuracy that cannot be achieved by using simulatorsbased on physical models is achieved.

The present disclosure improves the simulation accuracy of themanufacturing process.

SUMMARY

According to one aspect of the present disclosure, with respect to aninference method performed by at least one processor, the methodincludes inputting, by the at least one processor, into a learned model,non-processed object image data of a second object and data related to asecond process for the second object, and inferring, by the at least oneprocessor using the learned model, processed object image data of thesecond object on which the second process has been performed. Thelearned model has been trained so that an output obtained in response tonon-processed object image data of a first object and data related to afirst process for the first object being input approaches processedobject image data of the first object on which the first process hasbeen performed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing illustrating an example of an overall configurationof a simulation system;

FIG. 2 is a drawing illustrating an example of a hardware configurationof respective devices constituting the simulation system;

FIG. 3 is a drawing illustrating an example of training data;

FIG. 4 is a drawing illustrating an example of a functionalconfiguration of a learning unit of a learning device according to afirst embodiment;

FIG. 5 is a drawing illustrating an example of a functionalconfiguration of a data shaping unit of the learning device according tothe first embodiment;

FIG. 6 is a drawing illustrating a specific example of a processperformed by the data shaping unit of the learning device according tothe first embodiment;

FIG. 7 is a drawing illustrating a specific example of a processperformed by a dry etching learning model of the learning deviceaccording to the first embodiment;

FIG. 8 is a flowchart illustrating a flow of a learning process;

FIG. 9 is a drawing illustrating an example of a functionalconfiguration of an executing unit of an inference device;

FIG. 10A and FIG. 10B are drawings illustrating the simulation accuracyof a dry etching learned model;

FIG. 11A and FIG. 11B are drawings illustrating the simulation accuracyof a deposition learned model;

FIG. 12 is a drawing illustrating an example of a functionalconfiguration of a data shaping unit of a learning device according to asecond embodiment;

FIG. 13 is a drawing illustrating a specific example of a processperformed by a dry etching learning model of the learning deviceaccording to the second embodiment;

FIG. 14 is a drawing illustrating an example of a functionalconfiguration of a learning unit of a learning device according to athird embodiment;

FIG. 15A and FIG. 15B are drawings illustrating an example of afunctional configuration of a data shaping unit of a learning deviceaccording to a fourth embodiment; and

FIG. 16 is a drawing illustrating an application example of theinference device.

DETAILED DESCRIPTION

In the following, embodiments will be described in detail with referenceto the accompanying drawings. In the present specification and thedrawings, components having substantially the same functionalconfiguration are referenced by the same reference numerals, andoverlapping description is omitted.

First Embodiment Overall Configuration of a Simulation System

First, an overall configuration of a simulation system that simulates asemiconductor manufacturing process will be described. FIG. 1 is adrawing illustrating an example of the overall configuration of thesimulation system. As illustrated in FIG. 1, a simulation system 100includes a learning device 120, and an inference device 130. Variousdata and various information used in the simulation system 100 areobtained from semiconductor manufacturers, databases of semiconductormanufacturing device manufacturers, and the like.

As illustrated in the upper part of FIG. 1, predetermined parameter data(which will be described later in detail) is set in a semiconductormanufacturing device 110. When multiple wafers (objects) to be processed(i.e., non-processed wafers) are transferred, a process corresponding toeach manufacturing process (e.g., dry etching or deposition) isperformed.

Some non-processed wafers among the multiple non-processed wafers aretransferred to a measuring device 111 and the shape is measured by themeasuring device 111 at various positions. Then, the measuring device111 generates, for example, non-processed object image data (i.e.,two-dimensional image data) representing a cross-sectional shape at eachposition of the wafer to be processed. Here, the measuring device 111includes a scanning electron microscope (SEM), a length scanningelectron microscope (CD-SEM), a transmission electron microscope (TEM),an atomic force microscope (AFM), and the like. Further, it is assumedthat various metadata such as the magnification ratio of the microscopeis associated with the non-processed object image data generated by themeasuring device 111.

An example of FIG. 1 indicates a state in which the measuring device 111generates non-processed object image data having file names of “shapedata LD001”, “shape data LD002”, “shape data LD003”, and so on, as thenon-processed object image data.

When the process corresponding to each manufacturing process isperformed, a processed wafer is transferred from the semiconductormanufacturing device 110. The semiconductor manufacturing device 110measures the environment during processing when the processcorresponding to each manufacturing process is performed on thenon-processed wafer, and the semiconductor manufacturing device 110retains a measurement result as environmental information.

Some processed wafers among multiple processed wafers that aretransferred from the semiconductor manufacturing device 110 as theprocessed wafers are transferred to a measuring device 112 and the shapeis measured by the measuring device 112 at various positions. Then, themeasuring device 112, for example, generates processed object image data(i.e., two-dimensional image data) representing a cross-sectional shapeat each position of the processed wafer. Similarly with the measuringdevice 111, the measuring device 112 includes a scanning electronmicroscope (SEM), a length scanning electron microscope (CD-SEM), atransmission electron microscope (TEM), an atomic force microscope(AFM), and the like.

The example of FIG. 1 indicates a state in which the measuring device112 generates processed object image data having file names of “shapedata LD001′”, “shape data LD002′”, “shape data LD003′”, and so on, asthe processed object image data.

The non-processed object image data generated by the measuring device111, the parameter data set to the semiconductor manufacturing device110 and the environmental information retained by the semiconductormanufacturing device 110, and the processed object image data generatedby the measuring device 112 are collected by the learning device 120 astraining data. The learning device 120 stores the collected trainingdata in a training data storage unit 123. The parameter data set to thesemiconductor manufacturing device 110 and the environmental informationretained in the semiconductor manufacturing device 110 are given datarelating to a process corresponding to a manufacturing process that thesemiconductor manufacturing device 110 performs on the non-processedwafer (object). As described above, the given data related to theprocess corresponding to the manufacturing process that is performed canbe used as the training data, so that factors correlating with events ofthe manufacturing process can be reflected in machine learning.

A data shaping program and a learning program are installed in thelearning device 120, and when the programs are executed, the learningdevice 120 functions as a data shaping unit 121 and a learning unit 122.

The data shaping unit 121 is an example of a processing unit. The datashaping unit 121 reads the training data stored in the training datastorage unit 123 and processes a portion of the read training data intoa predetermined format suitable for being input to the learning model bythe learning unit 122.

The learning unit 122 performs machine learning on the learning model byusing the read training data (which includes the training data processedby the data shaping unit 121) to generate a learned model of thesemiconductor manufacturing process. The learned model generated by thelearning unit 122 is provided to the inference device 130.

A data shaping program and an execution program are installed in theinference device 130, and when the programs are executed, the inferencedevice 130 functions as a data shaping unit 131 and an executing unit132.

The data shaping unit 131 is an example of a processing unit. The datashaping unit 131 obtains the non-processed object image data generatedby the measuring device 111 and the parameter data and the environmentalinformation input to the inference device 130. The data shaping unit 131processes the obtained parameter data and the obtained environmentalinformation in a predetermined format suitable for being input into thelearned model by the executing unit 132.

The executing unit 132 inputs the non-processed object image data, andthe parameter data and the environmental information, processed in thepredetermined format in the data shaping unit 131, into the learnedmodel and performs a simulation to output (or performs an inference toobtain) the processed object image data (a simulation result).

A user of the inference device 130 verifies the learned model bycontrasting the processed object image data output by the executing unit132 executing the simulation using the learned model with thecorresponding non-processed object image data generated by the measuringdevice 112.

Specifically, the user of the inference device 130 contrasts thefollowing images:

-   -   the processed object image data output from the executing unit        132 by inputting the non-processed object image data, the        parameter data, and the environmental information to the data        shaping unit 131    -   the processed object image data generated when, after the        non-processed wafer is processed by the semiconductor        manufacturing device 110, the processed wafer is measured by the        measuring device 112        This enables the user of the inference device 130 to calculate        the simulation error of the learned model and verify the        simulation accuracy.

When the verification is completed, given processed object image data,given parameter data and given environmental information are input tothe inference device 130, and various simulations are performed.

Hardware Configuration of Each Device Constituting the Simulation System

Next, a hardware configuration of each device (i.e., the learning device120 and the inference device 130) constituting the simulation system 100will be described with reference to FIG. 2. FIG. 2 is a drawingillustrating an example of the hardware configuration of each deviceconstituting the simulation system.

Since the hardware configuration of the learning device 120 issubstantially the same as the hardware configuration of the inferencedevice 130, the hardware configuration of the learning device 120 willbe described here.

FIG. 2 is a drawing illustrating an example of the hardwareconfiguration of the learning device. As illustrated in FIG. 2, thelearning device 120 includes a central processing unit (CPU) 201 and aread only memory (ROM) 202. The learning device 120 also includes arandom access memory (RAM) 203 and a graphics processing unit (GPU) 204.The processor (processing circuit or processing circuitry), such as theCPU 201 and the GPU 204, and the memory, such as the ROM 202 and the RAM203, form what is called a computer.

The learning device 120 further includes an auxiliary storage device205, an operating device 206, a display device 207, an interface (I/F)device 208, and a drive device 209. Each hardware component of thelearning device 120 is interconnected to one another through a bus 210.

The CPU 201 is an arithmetic device that executes various programs(e.g., a data shaping program, a learning program, and the like)installed in the auxiliary storage device 205.

The ROM 202 is a non-volatile memory that functions as a main storagedevice. The ROM 202 stores various programs, data, and the like that arenecessary for the CPU 201 to execute various programs installed in theauxiliary storage device 205. Specifically, the ROM 202 stores a bootprogram such as Basic Input/Output System (BIOS), Extensible FirmwareInterface (EFI), or the like.

The RAM 203 is a volatile memory such as a dynamic random access memory(DRAM) or a static random access memory (SRAM) and functions as a mainstorage device. The RAM 203 provides a workspace in which variousprograms installed in the auxiliary storage device 205 are deployed whenthe various programs are executed by the CPU 201.

The GPU 204 is an arithmetic device for image processing. When variousprograms are executed by the CPU 201, the GPU 204 performs high-speedarithmetic operations on various image data by Parallel processing.

The auxiliary storage device 205 is a storage unit that stores variousprograms, various image data on which image processing is performed bythe GPU 204 when various programs are executed by the CPU 201, and thelike. For example, the training data storage unit 123 is achieved by theauxiliary storage device 205.

The operating device 206 is an input device used when an administratorof the learning device 120 inputs various instructions to the learningdevice 120. The display device 207 is a display that displays aninternal state of the learning device 120. The I/F device 208 is aconnection device for connecting and communicating with another device.

The drive device 209 is a device in which a recording medium 220 is set.Here, the recording medium 220 includes a medium that recordsinformation optically, electrically, or magnetically, such as a CD-ROM,a flexible disk, a magneto-optical disk, or the like. The recordingmedium 220 may include a semiconductor memory that electrically recordsinformation, such as a ROM, or a flash memory, or the like.

The various programs installed in the auxiliary storage device 205 areinstalled, for example, when the distributed recording medium 220 is setin the drive device 209 and various programs recorded in the recordingmedium 220 are read by the drive device 209. Alternatively, variousprograms to be installed in the auxiliary storage device 205 may beinstalled by downloading through a network, which is not illustrated.

Description of the Training Data

Next, the training data stored in the training data storage unit 123will be described. FIG. 3 is a drawing illustrating an example of thetraining data. As illustrated in FIG. 3, the training data 300 includes“process”, “job ID”, “non-processed object image data”, “parameterdata”, and “processed object image data” as items of information.

In the “process”, a name indicating the semiconductor manufacturingprocess is stored. The example of FIG. 3 indicates a state in which twonames “dry etching” and “deposition” are stored as the “process”.

In the “job ID”, an identifier for identifying a job performed by thesemiconductor manufacturing device 110 is stored.

The example of FIG. 3 indicates a state in which “PJ001” and “PJ002” arestored as the “job ID” of dry etching. The example of FIG. 3 alsoindicates a state in which “PJ101” is stored as the “job ID” ofdeposition.

In the “non-processed object image data”, the file name of thenon-processed object image data generated by the measuring device 111 isstored. The example of FIG. 3 indicates that when the job ID is “PJ001”,the non-processed object image data having a file name of “shape dataLD001” is generated by the measuring device 111 for one of thenon-processed wafers in a lot (i.e., a wafer group) of the job.

The example of FIG. 3 indicates that when the job ID is “PJ002”, thenon-processed object image data having a file name of “shape data LD002”is generated by the measuring device 111 for one of the non-processedwafers in a lot (i.e., a wafer group) of the job. Further, the exampleof FIG. 3 indicates that when the job ID is “PJ101”, the non-processedobject image data having a file name of “shape data LD101” is generatedby the measuring device 111 for one of the non-processed wafers in a lot(i.e., a wafer group) of the job.

In the “parameter data”, a parameter representing a predeterminedprocessing condition set when the non-processed wafer is processed inthe semiconductor manufacturing device 110 is stored. The example ofFIG. 3 illustrates that “parameter 001_1”, “parameter 001_2”, “parameter001_3”, and so on have been set when the job having job ID of “PJ001” isperformed in the semiconductor manufacturing device 110.

For example, “parameter 001_1”, “parameter 001_2”, “parameter 001_3”,and so on include the following:

-   -   data set to the semiconductor manufacturing device 110 as set        values, such as pressure (i.e., pressure in a chamber), power        (i.e., power of a high-frequency power source), gas (i.e., the        gas flow rate), and temperature (i.e., the temperature in the        chamber or of a surface of the wafer)    -   data set to the semiconductor manufacturing device 110 as target        values, such as a critical dimension (CD), depth, the taper        angle, the tilting angle, and bowing    -   information related to a hardware configuration of the        semiconductor manufacturing device 110

In the “environmental information”, information indicating theenvironment during processing of the non-processed wafer that ismeasured when the non-processed wafer is processed in the semiconductormanufacturing device 110 is stored. The example of FIG. 3 indicates thatwhen the semiconductor manufacturing device 110 performs a processhaving job ID of “PJ001”, “environment data 001_1”, “environment data001_2”, “environment data 001_3”, and so on are measured asenvironmental information.

For example, “environment data 001_1”, “environment data 001_2”,“environment data 001_3”, and so on include the following:

-   -   data (mainly data related to a current and voltage) output from        semiconductor manufacturing device 110 during processing, such        as voltage peak to peak (Vp-p), DC self-bias voltage (Vdc),        light emission intensity by optical emission spectroscopy (OES),        and reflected wave power (Reflect)    -   data measured during processing (mainly data related to light as        well as data related to the temperature and pressure), such as        plasma density, ion energy, and ion flux

In the “processed object image data”, a file name of the processedobject image data generated by the measuring device 112 is stored. Theexample of FIG. 3 indicates that when the job ID is “PJ001”, themeasuring device 112 generates the processed object image data having afile name of “shape data LD001′”.

The example of FIG. 3 indicates that when the job ID is “PJ002”, themeasuring device 112 generates the processed object image data having afile name of “shape data LD002′”. Further, the example of FIG. 3indicates that when the job ID is “PJ101”, the measuring device 112generates the processed object image data having a file name of “shapedata LD101′”.

Functional Configuration of the Learning Device

Next, functional configurations of respective units (i.e., the datashaping unit 121 and the learning unit 122) of the learning device 120will be described in detail.

(1) Details of the Functional Configuration of the Learning Unit

First, a functional configuration of the learning unit 122 of thelearning device 120 will be described in detail. FIG. 4 is a drawingillustrating an example of the functional configuration of the learningunit of the learning device according to the first embodiment. Asillustrated in FIG. 4, the learning unit 122 of the learning device 120includes a dry etching learning model 420, a deposition learning model421, a comparing unit 430, and a modifying unit 440.

The non-processed object image data, the parameter data, and theenvironmental information of the training data 300 stored in thetraining data storage unit 123 are read by the data shaping unit 121 andinput to a corresponding learning model. In the present embodiment, theparameter data and the environmental information are processed into apredetermined format by the data shaping unit 121 and input to thecorresponding learning model. However, the parameter data and theenvironmental information may be previously processed into thepredetermined format, and data that has been processed into thepredetermined format may be read by the data shaping unit 121 and inputto the corresponding learning model.

Into the dry etching learning model 420, the non-processed object imagedata, and the parameter data and the environmental information processedin the predetermined format by the data shaping unit 121 (which arelimited to the data to which the “dry etching” of the “process” isassociated) are input. When the non-processed object image data, and theparameter data and the environmental information processed into thepredetermined format are input, the dry etching learning model 420outputs an output result. The dry etching learning model 420 inputs theoutput result to the comparing unit 430.

Similarly, into the deposition learning model 421, the non-processedobject image data, and the parameter data and the environmentalinformation processed in the predetermined format by the data shapingunit 121 (which are limited to the data to which the “deposition” of the“process” is associated) are input. When the non-processed object imagedata, and the parameter data and the environmental information processedin the predetermined format are input, the deposition learning model 421outputs an output result. The deposition learning model 421 inputs theoutput result to the comparing unit 430.

The comparing unit 430 compares the output result input from the dryetching learning model 420 with the processed object image data of thetraining data 300 (i.e., the processed object image data to which the“dry etching” of the “process” is associated) and notifies the modifyingunit 440 of differential information.

Similarly, the comparing unit 430 compares the output result input fromthe deposition learning model 421 with the processed object image dataof the training data 300 (i.e., the processed object image data to whichthe “deposition” of the “process” is associated) and notifies themodifying unit 440 of differential information.

The modifying unit 440 updates a model parameter of the dry etchinglearning model 420 or the deposition learning model 421 based on therespective differential information notified by the comparing unit 430.The differential information used to update the model parameter may be asquared error or an absolute error.

As described above, the learning unit 122 inputs the non-processedobject image data, and the parameter data and the environmentalinformation processed in the predetermined format into the learningmodel and updates the model parameter by using machine learning so thatthe output result output from the learning model approaches theprocessed object image data.

This enables the learning unit 122 to reflect the processed object imagedata in which the effect of each event in the semiconductormanufacturing process appears in machine learning and to perform machinelearning of a relationship between these events, and the parameter dataand the environmental information.

(2) Details of the Functional Configuration of the Data Shaping Unit

Next, the functional configuration of the data shaping unit 121 of thelearning device 120 will be described in detail. FIG. 5 is a drawingillustrating an example of the functional configuration of the datashaping unit of the learning device according to the first embodiment.As illustrated in FIG. 5, the data shaping unit 121 includes a shapedata obtaining unit 501, a channel data generator 502, a one-dimensionaldata obtaining unit 511, a one-dimensional data expanding unit 512, anda concatenating unit 520.

The shape data obtaining unit 501 reads the processed object image dataof the training data 300 from the training data storage unit 123 andnotifies the channel data generator 502.

The channel data generator 502 is an example of a generator. The channeldata generator 502 obtains the non-processed object image data notifiedby the shape data obtaining unit 501 (here, it is assumed that the imagedata is represented by pixel values corresponding to the compositionratio (or the content ratio) of each material). The channel datagenerator 502 generates image data having multiple channelscorresponding to types of the materials from the obtained non-processedobject image data. Hereinafter, the image data having the channelscorresponding to the types of the materials is referred to as thechannel data. For example, the channel data generator 502 generateschannel data including an air layer and four channel data respectivelyincluding four material layers from the non-processed object image data.

The channel data generator 502 notifies the concatenating unit 520 ofthe generated multiple channel data. In the present embodiment, althoughthe channel data generator 502 generates the channel data, the channeldata may be previously generated. In this case, the channel datagenerator 502 reads the previously generated channel data and notifiesthe concatenating unit 520.

The one-dimensional data obtaining unit 511 reads the parameter data andthe environmental information of the training data 300 from the trainingdata storage unit 123 and notifies the one-dimensional data expandingunit 512.

The one-dimensional data expanding unit 512 processes the parameter dataand the environmental information notified from the one-dimensional dataobtaining unit 511 into a predetermined format in accordance with thesize of the non-processed object image data (i.e., a format of atwo-dimensional array in accordance with the width and the height of thenon-processed object image data).

Here, in the parameter data, numerical values of parameters such as“parameter 001_1”, “parameter 001_2”, “parameter 001_3”, and so on arearranged in one dimension. Specifically, in the parameter data,numerical values of N types of parameters are arranged in one dimension.

Thus, the one-dimensional data expanding unit 512 extracts a numericalvalue of one of the N types of parameters included in the parameter dataone by one, and arranges the extracted numerical values in twodimensions in accordance with the width and the height of thenon-processed object image data. As a result, the one-dimensional dataexpanding unit 512 generates N parameter data respectively arranged intwo dimensions.

The one-dimensional data expanding unit 512 notifies the concatenatingunit 520 of the N parameter data respectively arranged in twodimensions.

Similarly, in the environmental information, for example, numericalvalues of environmental information, such as “environment data 001_1”,“environment data 001_2”, “environment data 001_3”, and so on arearranged in one dimension. Specifically, numerical values of M types ofthe environmental data are arranged in one dimension.

Thus, the one-dimensional data expanding unit 512 extracts a numericalvalue of one of M types of environmental data included in theenvironmental information one by one, and arranges the extractednumerical values in two dimensions in accordance with the width and theheight of the non-processed object image data. As a result, theone-dimensional data expanding unit 512 generates M environmentalinformation respectively arranged in two dimensions.

The one-dimensional data expanding unit 512 notifies the concatenatingunit 520 of the M environmental information respectively arranged in twodimensions.

The concatenating unit 520 concatenates N parameter data and Menvironmental information respectively arranged in two dimensionsnotified by the one-dimensional data expanding unit 512 to the multiplechannel data notified by the channel data generator 502 as new channelsand generates the concatenated data. In the present embodiment, theconcatenating unit 520 generates the concatenated data, but theconcatenated data may have been previously generated. In this case, theconcatenating unit 520 reads the previously generated concatenated dataand inputs the concatenated data into the learning model.

Specific Example of a Process Performed by Each Unit of the LearningDevice

Next, a specific example of a process performed by the above-describeddata shaping unit 121 and a process performed by the dry etchinglearning model 420 in the learning unit 122 among the respective unitsof the learning device 120 will be described.

(1) Specific Example of the Process Performed by the Data Shaping Unit

FIG. 6 is a drawing illustrating a specific example of the processperformed by the data shaping unit. In FIG. 6, the non-processed objectimage data 600 is, for example, non-processed object image data having afile name of “shape data LD001”.

As illustrated in FIG. 6, the non-processed object image data 600includes a layer of air, a layer of a material A, a layer of a materialB, a layer of a material C, and a layer of a material D. In this case,the channel data generator 502 generates channel data 601, 602, 603,604, and 605.

As illustrated in FIG. 6, in parameter data 610, for example, numericalvalues of respective parameters (e.g., “parameter 001_1”, “parameter001_2”, “parameter 001_3”, and so on) are arrayed in one dimension.

Further, as illustrated in FIG. 6, in the environmental information 620,for example, numerical values of respective environment data (e.g.,“environment data 001_1”, “environment data 001_2”, “environment data001_3”, and so on) are arrayed in one dimension.

In this case, the one-dimensional data expanding unit 512 arrays theparameter 001_1 in two dimensions (i.e., the one-dimensional dataexpanding unit 512 arrays the same values vertically and horizontally)in accordance with the width and the height of the non-processed objectimage data 600.

Similarly, the one-dimensional data expanding unit 512 arrays theparameter 001_2 in two dimensions in accordance with the width and theheight of the non-processed object image data 600. Similarly, theone-dimensional data expanding unit 512 arrays the parameter 001_3 intwo dimensions in accordance with the width and the height of thenon-processed object image data 600.

The one-dimensional data expanding unit 512 arrays the environment data001_1 in two dimensions (i.e., the one-dimensional data expanding unit512 arrays the same values vertically and horizontally) in accordancewith the width and the height of the non-processed object image data600. Similarly, the one-dimensional data expanding unit 512 arrays theenvironment data 001_2 in two dimensions in accordance with the widthand the height of the non-processed object image data 600.

Similarly, the one-dimensional data expanding unit 512 arrays theenvironment data 001_3 in two dimensions in accordance with the widthand the height of the non-processed object image data 600.

Parameter data 611, 612, 613, and so on arrayed in two dimensions andenvironmental information 621, 622, 623, and so on arrayed in twodimensions are concatenated by the concatenating unit 520 as newchannels with the channel data 601, 602, 603, 604, and 605, andconcatenated data 630 is generated.

(2) Specific Example of a Process Performed Using the Dry EtchingLearning Model

Next, a specific example of a process performed using the dry etchinglearning model 420 in the learning unit 122 will be described. FIG. 7 isa drawing illustrating a specific example of the process performed usingthe dry etching learning model of the learning device according to thefirst embodiment. As illustrated in FIG. 7, in the present embodiment, alearning model based on a U-shaped convolutional neural network (CNN)(which is what is called UNET) is used as the dry etching learning model420.

When the UNET is used, typically, image data is input and image data isoutput. Thus, the UNET is used as a learning model of the learning unit122, so that non-processed object image data of the semiconductormanufacturing process can be input and the processed object image dataof the semiconductor manufacturing process can be output.

With respect to the above, when the UNET is used, data that is not in animage data format is required to be processed into an image data format.The one-dimensional data expanding unit 512 of the data shaping unit 121described above is configured to array the parameter data and theenvironment data in two dimensions in order to process the data to beinput to the UNET into an image data format. The parameter data and theenvironmental information can be input to the UNET, so that machinelearning can be performed using factors correlating with events of thedry etching.

The example of FIG. 7 illustrates a state in which the concatenated data630 is input to the dry etching learning model 420 using the UNET, andan output result 700 including multiple channel data is output.

Here, in the example of FIG. 7, a specific example of the processperformed by using the dry etching learning model 420 is illustrated.However, a specific example of the process performed by using thedeposition learning model 421 is substantially the same.

Flow of a Learning Process

Next, a flow of a learning process will be described. FIG. 8 is aflowchart illustrating the flow of the learning process.

In step S801, the measuring device 111 measures the shape of thenon-processed wafer to be processed by the semiconductor manufacturingdevice 110 at various positions and generates the non-processed objectimage data.

In step S802, the measuring device 112 measures the shape of the waferprocessed by the semiconductor manufacturing device 110 at variouspositions and generates the processed object image data.

In step S803, the learning device 120 obtains the parameter data set tothe semiconductor manufacturing device 110 and the environmentalinformation obtained by measuring the environment during processing whenthe semiconductor manufacturing device 110 performs the processcorresponding to each manufacturing process.

In step S804, the learning device 120 stores the non-processed objectimage data generated by the measuring device 111, the processed objectimage data generated by the measuring device 112, and the parameter dataand the environmental information that are obtained in the training datastorage unit 123 as training data.

In step S805, the data shaping unit 121 of the learning device 120 readsthe non-processed object image data, the parameter data, and theenvironmental information from the training data storage unit 123 andgenerates the concatenated data.

In step S806, the learning unit 122 of the learning device 120 performsmachine learning on the learning model by using the concatenated data asan input and the processed object image data as an output, and generatesa learned model.

In step S807, the learning unit 122 of the learning device 120 transmitsthe generated learned model to the inference device 130.

Functional Configuration of the Inference Device

Next, a functional configuration of the inference device 130 will bedescribed in detail. In respective units of the inference device 130(i.e., the data shaping unit 131 and the executing unit 132), thedetails of the functional configuration of the data shaping unit 131 aresubstantially the same as the details of the functional configuration ofthe data shaping unit 121 of the learning device 120. Thus, descriptionof the functional configuration of the data shaping unit 131 will beomitted here, and the functional configuration of the executing unit 132will be described in detail below.

FIG. 9 is a drawing illustrating an example of the functionalconfiguration of the executing unit of the inference device. Asillustrated in FIG. 9, the executing unit 132 of the inference device130 includes a dry etching learned model 920, a deposition learned model921, and an output unit 930.

When the non-processed object image data generated by the measuringdevice 111 (e.g., data that is not used for machine learning) isobtained, and the parameter data and the environmental data are input tothe inference device 130, the data shaping unit 131 generates theconcatenated data. The data shaping unit 131 inputs the concatenateddata to the corresponding learned model. The example of FIG. 9illustrates a state in which non-processed object image data having filenames of “shape data SD001”, “shape data SD002”, . . . , so on isobtained as the non-processed object image data generated by themeasuring device 111.

The dry etching learned model 920 performs a simulation in response tothe concatenated data being input by the data shaping unit 131. The dryetching learned model 920 notifies the output unit 930 of an outputresult that is output by performing the simulation.

Similarly, the deposition learned model 921 performs a simulation inresponse to the concatenated data being input by the data shaping unit131. The deposition learned model 921 notifies the output unit 930 of anoutput result that is output by performing the simulation.

Here, the non-processed object image data generated by the measuringdevice 111 is input. However, any non-processed object image data can beinput to the dry etching learned model 920 and the deposition learnedmodel 921.

The output unit 930 generates processed object image data (e.g., imagedata having a file name of “shape data SD001″”) from the output resultnotified from the dry etching learned model 920 and outputs theprocessed object image data as a simulation result. Similarly, theoutput unit 930 generates processed image data (e.g., image data havinga file name of “shape data SD101″”) from the output result notified fromthe deposition learned model 921, and outputs the processed object imagedata as a simulation result.

Here, it is assumed that the user of the inference device 130 inputs theparameter data and the environmental information that are the same asthe parameter data set to the semiconductor manufacturing device 110 andthe environmental information retained by the semiconductormanufacturing device 110. In this case, the user of the inference device130 can contrast the processed object image data output from the outputunit 930 and the processed object image data generated from themeasuring device 112 (e.g., the image data having a file name of “shapedata SD001′”). As a result, the user of the inference device 130 canverify the simulation accuracy of the inference device 130.

FIG. 10A and FIG. 10B are drawings illustrating the simulation accuracyof the dry etching learned model. FIG. 10A illustrates the non-processedobject image data generated by the measuring device 111 (i.e., the imagedata having a file name of “shape data SD001”), and the processed objectimage data generated by the measuring device 112 (i.e., the image datahaving a file name of “shape data SD001′”) as a comparison target.

With respect to the above, FIG. 10B illustrates the non-processed imageobject data (i.e., the image data having a file name of “shape dataSD001′”) and the processed image data (i.e., the image data having afile name of “shape data SD001″”) obtained when the simulation isperformed by using the dry etching learned model 920.

When the processed object image data of FIG. 10A (i.e., the image datahaving a file name of “shape data SD001′”) and the processed objectimage data of FIG. 10B (i.e., the image data having a file name of“shape data SD001″”) are contrasted, there is no difference betweenthem. Thus, the dry etching learned model 920 can achieve a highlyaccurate simulation for the dry etching performed by the semiconductormanufacturing device 110.

Similarly, FIG. 11A and FIG. 11B are drawings illustrating thesimulation accuracy of the deposition learned model. FIG. 11Aillustrates the non-processed object image data generated by themeasuring device 111 (i.e., the image data having a file name of “shapedata SD101”) and the processed object image data generated by themeasuring device 112 (i.e., the image data having a file name of “shapedata SD101′”) as a comparison target.

With respect to the above, FIG. 11B illustrates the non-processed objectimage data (i.e., the image data having a file name of “shape dataSD101”) and the processed object image data (i.e., the image data havinga file name of “shape data SD101″”) obtained when the simulation isperformed by using the deposition learned model 921.

When the processed object image data of FIG. 11A (i.e., the image datahaving a file name of “shape data SD101′”) and the processed objectimage data of FIG. 11B (i.e., the image data having a file name of“shape data SD101″”) are contrasted, there is no difference betweenthem. Thus, the deposition learned model 921 can achieve a highlyaccurate simulation for the deposition performed by the semiconductormanufacturing device 110.

Here, in each learned model of the executing unit 132, the simulationaccuracy can be improved in comparison with a general physical model(i.e., a model in which a semiconductor manufacturing process isidentified based on a physical law).

This is because, in a general physical model, each event that cannot berepresented by a physical equation cannot be reflected in a simulation,but in a learning model, each event that causes an effect appears in theprocessed object image data can be reflected in machine learning.

Additionally, in the learning model according to the present embodiment,since factors correlating with events in the semiconductor manufacturingprocess (i.e., the parameter data and the environmental information) areinput, machine learning can be performed on the relationship betweenevents and factors.

Here, each event that cannot be represented by the physical equationincludes, for example, an event in which the gas in a chamber becomesnon-uniform in the dry etching. Additionally, an event in which etchedparticles adhere as deposition and the like are included. In thedeposition, for example, an event in which particles are rebounded oneor more times and then adhere and the like are included.

For these events, in a general physical model, the gas in a chamber istreated as uniform during dry etching. Also, in a general physicalmodel, particles are treated as adhering to a first contact point duringdeposition.

With respect to the above, in a learned model, processed object imagedata in which the effects of these events appear can be reflected inmachine learning and machine learning can be performed on therelationship between these events, and the parameter data and theenvironmental information. Thus, in a learned model, the simulationaccuracy can be improved in comparison with a general physical model.

As described above, a learned model can achieve simulation accuracy thatcannot be achieved by a simulator based on a physical model. Inaddition, a learned model can reduce the simulation time in comparisonwith a simulator based on a physical model. Further, a learned model hasadvantages, such as no need to create a rule manually and no need todevelop an appropriate physical equation as in a simulator based on aphysical model.

Summary

As is obvious from the above description, the learning device accordingto the first embodiment is configured to:

-   -   obtain the parameter data set to the semiconductor manufacturing        device when the wafer to be processed is processed and the        environmental information indicating environment during        processing of the wafer to be processed that is measured when        the wafer to be processed is processed    -   obtain the non-processed object image data that is an image        representing a shape of the wafer to be processed in the        semiconductor manufacturing device before processing    -   array the obtained parameter data and the obtained environmental        information in two dimensions in accordance with the width and        the height of the obtained non-processed object image data to        process the obtained parameter data and the obtained        environmental information into an image data format, and        concatenate the processed parameter data and the processed        environmental information with the non-processed object image to        generate concatenated data    -   perform machine learning by inputting the generated concatenated        data into a learning model based on a U-shaped convolutional        neural network so that an output result approaches the processed        object image data representing a shape of the wafer after        processing

This enables the learning device according to the first embodiment toreflect factors correlating with events of the manufacturing process inmachine learning and to generate a learned model that achieves a highlyaccurate simulation.

The inference device according to the first embodiment is configured to:

-   -   obtain non-processed object image data, the parameter data, and        the environmental information    -   array the obtained parameter data and the obtained environmental        information in two dimensions in accordance with the width and        the height of the obtained non-processed object image data to        process the obtained parameter data and the obtained        environmental information into an image data format, and        concatenate the processed parameter data and the processed        environmental information with the non-processed object image to        generate concatenated data    -   perform a simulation by inputting the generated concatenated        data into the learned model

This enables the inference device according to the first embodiment togenerate a learned model on which machine learning is performed by usingfactors correlating with events of the manufacturing process and toachieve a highly accurate simulation.

As described above, according to the first embodiment, in the simulationof the semiconductor manufacturing process, the simulation accuracy canbe improved.

Second Embodiment

In the above-described first embodiment, the parameter data and theenvironmental information are processed into the image data format inaccordance with the width and the height of the non-processed objectimage data, and are concatenated with the non-processed object imagedata to be input to a learning model (or a learned model).

However, a method of processing the parameter data and the environmentalinformation, and a method of inputting the processed parameter data andthe processed environmental information into a learning model (or alearned model) are not limited to this. For example, the processedparameter data and the processed environmental information may be inputto each layer of a learning model (or a learned model). When theparameter data and the environmental information are input to each layerof the learning model (or learned model), the parameter data and theenvironmental information may also be processed into a predeterminedformat used when the image data on which a convolution operation isperformed at each layer of the learning model (or the learned model) isconverted. In the following, a second embodiment will be describedfocusing on differences between the second embodiment and theabove-described first embodiment.

Functional Configuration of a Data Shaping Unit

First, a functional configuration of a data shaping unit of the learningdevice according to the second embodiment will be described in detail.FIG. 12 is a drawing illustrating an example of the functionalconfiguration of the data shaping unit of the learning device accordingto the second embodiment. A difference between this functionalconfiguration and the functional configuration of the data shaping unit121 illustrated in FIG. 5 is that a data shaping unit 1200 illustratedin FIG. 12 includes a concatenating unit 1201 and a normalizing unit1202.

The concatenating unit 1201 concatenates multiple channel data notifiedfrom the channel data generator 502 and then generates concatenateddata.

The normalizing unit 1202 normalizes the parameter data and theenvironmental information notified from the one-dimensional dataobtaining unit 511 and generates normalized parameter data andnormalized environmental information.

Specific Example of a Process Performed by a Learning Model

Next, a specific example of a process performed by a dry etchinglearning model will be described. FIG. 13 is a drawing illustrating aspecific example of the process performed by the dry etching learningmodel of the learning device according to the second embodiment.

As illustrated in FIG. 13, in the learning device according to thesecond embodiment, concatenated data 1310 generated by the concatenatingunit 1201 of the data shaping unit 1200 is input to a dry etchinglearning model 1300.

As illustrated in FIG. 13, in the learning device according to thesecond embodiment, the normalized parameter data and the normalizedenvironmental information that are generated by the normalizing unit1202 of the data shaping unit 1200 are input to the dry etching learningmodel 1300.

As illustrated in FIG. 13, the dry etching learning model 1300 includesa neural network 1301 that is a fully connected learning model inaddition to the UNET that is a CNN-based learning model.

In response to the normalized parameter data and the normalizedenvironmental information being input, the neural network 1301 outputspredetermined format values (e.g., coefficients γ and β of a linearequation) used to convert a value of each pixel of each image data onwhich a convolution operation is performed at each layer of the UNET.That is, the neural network 1301 has a function to process thenormalized parameter data and the normalized environmental informationinto a predetermined format (e.g., a format of coefficients of a linearequation).

In the example of FIG. 13, the UNET is composed of nine lavers, so thatthe neural network 1301 outputs (γ1, β1) to (γ9, β9) as coefficients ofthe linear equation. In the example illustrated in FIG. 13, due to spacelimitation, a pair of the coefficients of the linear equation is inputfor each laver, but multiple pairs of the coefficients of the linearequation are input for each layer for each channel data.

In each layer of the UNET, a value of each pixel of image data of eachchannel data (which is defined as “h” here) on which a convolutionoperation is performed is converted by using, for example, a linearequation: h×γ+β (i.e., in the first layer, h×γ₁+β₁).

Here, the coefficients (γ1, β1) to (γ9, β9) of the linear equation canbe regarded as an index indicating which image data is important amongimage data of respective channel data on which a convolution operationis performed in each layer of the UNET, for example. That is, the neuralnetwork 1301 performs a process of calculating an index indicating theimportance of each image data processed at each layer of the learningmodel based on the normalized parameter data and the normalizedenvironmental information.

Under the above-described configuration, when the concatenated data1310, the normalized parameter data, and the normalized environmentalinformation are input to the dry etching learning model 1300, the outputresult 700 including multiple channel data is output. The output result700 is compared with the processed object image data by the comparingunit 430 and differential information is calculated. In the learningdevice according to the second embodiment, the modifying unit 440updates model parameters of the UNET and model parameters of the neuralnetwork 1301 in the dry etching learning model 1300 based on thedifferential information.

As described above, the learning device according to the secondembodiment can extract highly important image data in each layer of theUNET based on the normalized parameter data and the normalizedenvironmental information when machine learning is performed on the dryetching learning model 1300.

Summary

As is clear from the above description, the learning device according tothe second embodiment is configured to:

-   -   obtain the parameter data set to the semiconductor manufacturing        device when the wafer to be processed is processed and the        environmental information indicating environment during        processing of the wafer to be processed that is measured when        the wafer to be processed is processed    -   obtain the non-processed object image data that is an image        representing a shape of the wafer to be processed in the        semiconductor manufacturing device before processing    -   normalize the obtained parameter data and the obtained        environmental information to process the obtained parameter data        and the obtained environmental information into a coefficient        format of a linear equation used to convert a value of each        pixel of each image data on which a convolution operation is        performed in each layer of the learning model    -   convert the value of each pixel of each image data on which a        convolution operation is performed in each layer by using the        linear equation when the learning unit performs machine learning

This enables the learning device according to the second embodiment toreflect factors correlating with events of the manufacturing process inmachine learning and to generate a learned model that achieves a highlyaccurate simulation.

Although the learning device has been described in the secondembodiment, when the executing unit performs a simulation in theinference device, substantially the same process is performed.

Third Embodiment

In the first and second embodiments described above, when the learningunit performs machine learning, a constraint condition specific to thesemiconductor manufacturing process is not particularly mentioned.However, constraint conditions specific to the semiconductormanufacturing process exist, and the simulation accuracy can be furtherimproved by reflecting a specific constraint condition in machinelearning performed by the learning unit (i.e., by reflecting domainknowledge in machine learning performed by the learning unit). In thefollowing, a third embodiment in which domain knowledge is reflectedwill be described focusing on differences between the third embodimentand the first and second embodiments described above.

Details of a Functional Configuration of the Learning Model

FIG. 14 is a drawing illustrating an example of a functionalconfiguration of a learning unit of a learning device according to thethird embodiment. An internal configuration within the learning modeldiffers from the functional configuration of the learning unit 122illustrated in FIG. 4. Here, the internal configuration within thelearning model will be described using the dry etching learning model1410, but the deposition learning model has substantially the sameinternal configuration.

As illustrated in FIG. 14, the dry etching learning model 1410 in thelearning unit 1400 includes a sigmoid function unit 1412 and amultiplier 1413 in addition to the UNET 1411.

The sigmoid function unit 1412 is an example of a processing unit. Asillustrated in FIG. 14, a first output result that is an output of theUNET 1411 is converted by a sigmoid function 1420 to output a secondoutput result 1421.

The multiplier 1413 obtains the second output result 1421 from thesigmoid function unit 1412. The multiplier 1413 obtains thenon-processed object image data from the data shaping unit 121.

The multiplier 1413 multiplies the obtained non-processed object imagedata by the obtained second output result 1421 to notify a final outputresult 1422 to the comparing unit 430.

As described above, the dry etching learning model 1410 is configured tooutput the final output result 1422 by multiplying the non-processedobject image data, so that the image data representing the etch ratio isoutput from the UNET 1411 as the first output result when machinelearning is performed on the dry etching learning model 1410.

Here, the etch rate indicates a value of the change rate that indicateshow much a layer of each material included in the non-processed objectimage data has been etched in the processed object image data. Byperforming machine learning on the dry etching learning model 1410, theetch rate approaches a value obtained by dividing the processed objectimage data by the non-processed object image data. However, the firstoutput result output from the UNET 1411 during machine learning may beany value.

In dry etching, there is a constraint condition (i.e., domain knowledge)that “materials do not increase over the course of processing” withrespect to the change in shape. Thus, in dry etching, the etch rate iswithin the range from 0 to 1.

Here, the sigmoid function unit 1412 is a function that converts anyvalue to a value from 0 to 1, and the above-described domain knowledgecan be reflected by the sigmoid function unit 1412 converting the firstoutput result to the second output result.

Although not illustrated in FIG. 14, substantially the same process canbe performed in the deposition learning model by providing the sigmoidfunction unit, the multiplier, and the like. Specifically, as the firstoutput result, image data representing the deposition rate is outputfrom the UNET when machine learning is performed on the depositionlearning model.

Here, the deposition rate indicates a value of the change rate thatindicates how much a thin film is deposited in the processed objectimage data for a layer of each material included in the non-processedobject image data. By performing machine learning on the depositionlearning model, the deposition rate approaches a value obtained bydividing a difference between the non-processed object image data andthe processed object image data by the non-processed object image data.However, the first output result output from the UNET during machinelearning may be any value.

In deposition, there is a constraint condition (i.e., domain knowledge)that “materials do not decrease over the course of processing” withrespect to the change in shape. Thus, in deposition, the deposition rateis within the range from 0 to 1.

As described above, the sigmoid function unit is a function thatconverts any value to a value from 0 to 1, and the domain knowledge canbe reflected by the sigmoid function unit converting the first outputresult to the second output result.

As described above, according to the learning unit 1400 of the learningdevice 120 of the third embodiment, the domain knowledge can bereflected in the machine learning, and the simulation accuracy can befurther improved.

Fourth Embodiment

In the first to third embodiments described above, the data shaping unitgenerates the concatenated data having a height and a width inaccordance with the height and width of the non-processed object imagedata. However, the width and the height of the concatenated datagenerated by the data shaping unit are determined as desired, and thedata shaping unit may be configured to compress the non-processed objectimage data and generate the concatenated data. In the following, afourth embodiment will be described focusing on differences between thefourth embodiment and the first to third embodiments described above.

Details of a Functional Configuration of a Data Shaping Unit

FIG. 15A and FIG. 15B are drawings illustrating an example of afunctional configuration of a data shaping unit of a learning deviceaccording to the fourth embodiment. FIG. 15A illustrates a data shapingunit 1510 in which a compressing unit 1511 is added to the data shapingunit 121 of the learning device according to the first embodiment.

The compressing unit 1511 compresses the non-processed object image dataobtained by the shape data obtaining unit 501. The compressing unit1511, for example, calculates an average value of pixel value of nadjacent pixels (n is an integer that is two or greater; for example,n=4 indicates two pixels in the vertical direction and two pixels in thehorizontal direction), and the calculated average value is defined as apixel value of one pixel that groups the n pixels. This enables thecompressing unit 1511 to compress the non-processed object image data bya factor of 1/n.

As described above, the compressing unit 1511 performs a compressionprocess so that the composition ratio (or the content ratio) of thematerials is maintained as much as possible over the course ofcompression in view of the fact that the non-processed object image datais image data representing the composition ratio (or the content ratio)of the materials. The compression rate of the compression processperformed by the compressing unit 1511 is not limited to an integermultiple, and in the compressing unit 1511, the compression process canbe performed with a desired compression rate.

Similarly, FIG. 15B illustrates a data shaping unit 1520 in which acompressing unit 1511 is added to the data shaping unit 1200 of thelearning device according to the second embodiment.

The compressing unit 1511 included in the data shaping unit 1520 hassubstantially the same function as the compressing unit 1511 included inthe data shaping unit 1510. Thus, the detailed description is omittedhere.

As described above, by adding the compressing unit 1511 to the datashaping unit 1510 or 1520, the size of the concatenated data that isinput to the learning units 122 and 1400 (or the executing unit 132) canbe reduced. As a result, according to the fourth embodiment, a learningtime period required when the learning units 122 and 1400 performmachine learning or a simulation time period required when the executingunit 132 performs the simulation can be reduced.

Other Embodiments

In the first embodiment described above, the dry etching learning model420 and the deposition learning model 421 are provided in the learningunit 122, and machine learning is performed separately using differenttraining data.

However, dry etching and deposition may be simultaneously performed in asemiconductor manufacturing process. Assuming such a case, one learningmodel may be provided in the learning unit 122 so that machine learningcan be performed with respect to a case in which dry etching anddeposition are performed simultaneously.

In this case, the learning unit 122 performs machine learning on the onelearning model by using training data including non-processed objectimage data before dry etching and deposition are performed and processedobject image data after dry etching and deposition are performed.

As described above, in a typical physical model, the simulator isrequired to be separately provided in dry etching and deposition.However, in a learning model, the simulator can be integrated.

In the above-described first embodiment, the data shaping unit 121processes both the parameter data and the environmental information in apredetermined format and inputs the parameter data and the environmentalinformation to a corresponding learning model. However, the data shapingunit 121 may process only the parameter data into a predetermined formatand input the parameter data to a corresponding learning model. That is,when the machine learning is performed on the learning model by thelearning unit 122, only the parameter data may be used without using theenvironmental information.

Similarly, in the above-described first embodiment, the data shapingunit 131 processes both the parameter data and the environmentalinformation into a predetermined format and inputs the parameter dataand the environmental information to a corresponding learned model.However, the data shaping unit 131 may process only the parameter datainto a predetermined format and input the parameter data to acorresponding learned model. That is, when the simulation is performedusing the learned model by the executing unit 132, only the parameterdata may be used and the environmental information is not used.

In the above-described first embodiment, the non-processed object imagedata and the processed object image data are two-dimensional image data.However, the non-processed object image data and the processed objectimage data are not limited to the two-dimensional image data, but may bethree-dimensional image data (which is what is called voxel data).

If the non-processed object image data is two-dimensional image data,the concatenated data is an array of the number×channels×width×height.

If the non-processed object image data is three-dimensional image data,the concatenated data is an array of the number ofchannels×width×height×depth.

In the above-described first embodiment, the two-dimensional image datais handled as it is. However, the two-dimensional image data may bemodified and handled or the three-dimensional image data may be modifiedand handled. For example, the three-dimensional image data may beobtained and two-dimensional image data of a predetermined cross sectionmay be generated and may be input as the non-processed object imagedata. Alternatively, three-dimensional image data may be generated basedon two-dimensional image data of successive predetermined cross sectionsand may be input as the non-processed object image data.

In the above-described first embodiment, the channel data generator 502generates channel data for a layer of air and respective layers ofmaterials. However, a method of generating the channel data is notlimited to this, but the channel data may be generated based on largerclassifications, such as oxides, silicon, organics, and nitrides, ratherthan based on specific film types.

In the first to fourth embodiments described above, the inference device130 outputs the processed object image data and terminates theprocessing in response to the non-processed object image data, theparameter data, and the environmental information being input. However,a configuration of the inference device 130 is not limited to this. Forexample, the processed object image data output in response to thenon-processed object image data, the parameter data, and theenvironmental information being input may be input again to theinference device 130 together with the corresponding parameter data andenvironmental information. This enables the inference device 130 tocontinuously output shape changes. The corresponding parameter data andenvironmental information can be changed as desired when the processedobject image data is input to the inference device 130 again.

In the first to fourth embodiments described above, no specific exampleapplication of the inference device 130 has been particularly mentioned.However, the inference device 130 may be applied to, for example,services provided to semiconductor manufacturers for searching anoptimal recipe, optimal parameter data, and an optimal hardwareconfiguration.

FIG. 16 is a drawing illustrating an example application of theinference device and illustrates an example in which the inferencedevice 130 is applied to a service providing system 1600.

The service providing system 1600, for example, connects to each officeof a semiconductor manufacturer through a network 1640 to obtainnon-processed object image data. The service providing system 1600stores the obtained non-processed object image data in a data storageunit 1602.

The inference device 130 reads the non-processed object image data fromthe data storage unit 1602 and performs a simulation while changing theparameter data and the environmental information. This enables a user ofthe inference device 130 to search for an optimal recipe, optimalparameter data, or an optimal hardware configuration.

The information providing device 1601 provides the optimal recipe andthe optimal parameter data searched by the user of the inference device130 to each branch of the semiconductor manufacturer.

As described above, by applying the inference device 130 to the serviceproviding system 1600, the service providing system 1600 can provide theoptimal recipe and the optimal parameter data for the semiconductormanufacturer.

In the first to fourth embodiments described above, the wafer to beprocessed is an object, but the object is not limited to the wafer to beprocessed, and may be, for example, a chamber inner wall of thesemiconductor manufacturing device 110, a surface of a part, or thelike.

In the first to fourth embodiments described above, a case in which themeasuring device 111 (or the measuring device 112) generates thenon-processed object image data (or the processed object image data) hasbeen described. However, the non-processed object image data (or theprocessed object image data) is not limited to image data generated bythe measuring device 111 (or the measuring device 112). For example, themeasuring device 111 (or the measuring device 112) may be configured togenerate multidimensional measurement data representing the shape of anobject and the learning device 120 may be configured to generate thenon-processed object image data (or the processed object image data)based on the measurement data.

The measurement data generated by the measuring device 111 (or themeasuring device 112) includes, for example, data including positioninformation and film type information. Specifically, the measurementdata includes data that is generated by the CD-SEM and that combinesposition information and CD length measurement data. Alternatively, themeasurement data includes data that is generated by X-ray or Ramanmethod and that combines two-dimensional or three-dimensional shape andinformation about film types and the like. That is, the multidimensionalmeasurement data representing the shape of an object includes variousexpression forms in accordance with types of measuring devices.

In the first to fourth embodiments described above, the learning device120 and the inference device 130 are illustrated separately, but may beconfigured as a single unit.

In the first to fourth embodiments described above, one computerconstitutes the learning device 120, but multiple computers mayconstitute the learning device 120. Similarly, in the first to fourthembodiments described above, one computer constitutes the inferencedevice 130, but multiple computers may constitute the inference device130.

In the first to fourth embodiments described above, the learning device120 and the inference device 130 have been applied to a semiconductormanufacturing process, but it is needless to say that they may beapplied to processes other than a semiconductor manufacturing process.The processes other than a semiconductor manufacturing process includemanufacturing processes other than a semiconductor manufacturing processand non-manufacturing processes.

In the first to fourth embodiments described above, the learning device120 and the inference device 130 are achieved by causing ageneral-purpose computer to execute various programs, but a method ofachieving the learning device 120 and the inference device 130 is notlimited to this.

For example, the learning device 120 and the inference device 130 may beachieved by a dedicated electronic circuit (i.e., hardware), such as anintegrated circuit (IC) that implements a processor, memory, and thelike. Multiple components may be implemented in one electronic circuit,one component may be implemented in multiple electronic circuits, andcomponents and electronic circuits may be implemented on a one-to-onebasis.

It should be noted that the present invention is not limited to theabove-described configurations, such as the configurations described inthe above-described embodiments, and combinations with other elements.In these respects, various modifications can be made within the scope ofthe invention without departing from the spirit of the invention, andthe configurations may be appropriately determined according to anapplication form.

What is claimed is:
 1. An inference method performed by at least oneprocessor, the method comprising: inputting, by the at least oneprocessor, into a learned model, non-processed object image data of asecond object and data related to a second process for the secondobject, and inferring, by the at least one processor using the learnedmodel, processed object image data of the second object on which thesecond process has been performed, wherein the learned model has beentrained so that an output obtained in response to non-processed objectimage data of a first object and data related to a first process for thefirst object being input approaches processed object image data of thefirst object on which the first process has been performed.
 2. Theinference method as claimed in claim 1, further comprising: processing,by the at least one processor, the data related to the second processinto a format in accordance with the non-processed object image data ofthe second object, wherein the inputting of the non-processed objectimage data of the second object and the data related to the secondprocess inputs the processed data related to the second process into thelearned model.
 3. The inference method as claimed in claim 2, whereinthe format in accordance with the non-processed object image data of thesecond object is a two-dimensional array in accordance with a width anda height of the non-processed object image data of the second object. 4.The inference method as claimed in claim 1, wherein the non-processedobject image data of the second object input into the learned model hasa plurality of channels corresponding to materials included in thesecond object.
 5. The inference method as claimed in claim 1, furthercomprising: inputting, by the at least one processor, the processedobject image data of the second object and data related to a thirdprocess into the learned model, and inferring, by the at least oneprocessor using the learned model, processed object image data of thesecond object on which the second process and the third process havebeen performed.
 6. The inference method as claimed in claim 1, whereinthe first process and the second process are of a same type.
 7. Theinference method as claimed in claim 1, wherein the learned modelincludes a neural network.
 8. The inference method as claimed in claim7, wherein the neural network outputs the processed object image data ofthe second object.
 9. The inference method as claimed in claim 7,wherein the processed object image data of the second object isgenerated based on the non-processed object image data of the secondobject and an output of the neural network.
 10. The inference method asclaimed in claim 9, wherein the neural network outputs informationrelated to a change rate relative to the non-processed object image dataof the second object.
 11. The inference method as claimed in claim 1,wherein each of the first process and the second process is a processcorresponding to a semiconductor manufacturing process.
 12. Theinference method as claimed in claim 11, wherein the processcorresponding to the semiconductor manufacturing process includes atleast one of etching or deposition.
 13. The inference method as claimedin claim 11, wherein the data related to the second process includesinformation related to a parameter indicating a processing conditionwhen a semiconductor manufacturing device performs the processcorresponding to the semiconductor manufacturing process.
 14. Theinference method as claimed in claim 11, wherein the data related to thesecond process includes environmental information measured when asemiconductor manufacturing device performs the process corresponding tothe semiconductor manufacturing process.
 15. The inference method asclaimed in claim 13, wherein the parameter includes at least one of avalue set to the semiconductor manufacturing device or a hardwareconfiguration of the semiconductor manufacturing device.
 16. Theinference method as claimed in claim 14, wherein the environmentalinformation includes at least one of data related to a current, datarelated to voltage, data related to light, data related to atemperature, or data related to pressure that are measured in thesemiconductor manufacturing device.
 17. An inference device comprising:at least one memory; and at least one processor, wherein the at leastone memory stores a learned model that has been trained so that anoutput obtained in response to non-processed object image data of afirst object and data related to a first process for the first objectbeing input approaches processed object image data of the first objecton which the first process has been performed, and wherein the at leastone processor is configured to: input, into the learned model,non-processed object image data of a second object and data related to asecond process for the second object, and infer, by using the learnedmodel, processed object image data of the second object on which thesecond process has been performed.
 18. The inference device as claimedin claim 17, wherein the at least one processor is further configuredto: input, into the learned model, the processed object image data ofthe second object and data related to a third process, and infer, byusing the learned model, processed object image data of the secondobject on which the second process and the third process have beenperformed.
 19. A model generating method performed by at least oneprocessor, the method comprising training a learning model so that anoutput of the learning model in response to non-processed object imagedata of an object and data related to a process for the object beinginput into the learning model approaches processed object image data ofthe object on which the process has been performed.
 20. A learningdevice comprising: at least one memory; and at least one processorconfigured to train a learning model so that an output of the learningmodel in response to non-processed object image data of an object anddata related to a process for the object being input into the learningmodel approaches processed object image data of the object on which theprocess has been performed.